Analisa Performa Arsitektur Transfer Learning Untuk Mengindentifikasi Penyakit Daun Pada Tanaman Pangan

Salah satu faktor gagal panen ialah serangan penyakit yang menyerang pada bagian daun pada tanaman. Solusi dari permasalahan ini yaitu dengan melakukan identifikasi dini penyakit tanaman pangan dengan memanfaatkan image classification dan deep learning menggunakan objek citra daun untuk mempercepat proses identifikasi penyakit pada daun tanaman pangan sehingga tidak mempengaruhi hasil produksi tanaman. Banyak penelitian yang sudah membuat penelitian memanfaatkan Image classification untuk klasifikasi penyakit tanaman berdasarkan citra daun menggunakan metode Transfer Learning. Namun pada penelitian terdahulu hanya menggunakan satu dua atau tiga arsitetur dan hanya mengunakan satu dataset saja untuk proses pengujian yang membuat tidak terlalu memberikan jawaban arsitektur mana yang mempunyai performa terbaik untuk membuat model klasifikasi penyakit berdasarkan citra daun tanaman. oleh karena itu diperlukan adanya perbandingan performa dari tiap model arsitektur untuk mengetahu arsitektur mana yang terbaik. Maka dari itu penelitian ini, peneliti akan melakukan eksperimen menggunakan lima arsitektur dan tiga dataset yang berbeda dengan enam sekenario pelatihan model dan selanjutnya kami melakukan anlisis perbandingan kinerja tiap sekenario pelatihan model. Hasilnya Penelitian ini dilakukan analisa hasil pelatihan dan pengujian yang sudah dilakukan arsitektur VGG 16 memiliki performa yang paling baik dibandingkan dengan arsitektur lainnya yang diujikan

Combining deep learning and X-ray imaging technology to assess tomato seed quality

ABSTRACT Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds

Análise de sementes associado a aprendizagem de máquina para identificar espécies florestais nativas

Orientador: Prof. Dr. Antonio Carlos NogueiraCoorientadores: Profª. Drª. Dagma Kratz e Prof. Dr. Richardson RibeiroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa : Curitiba, 31/07/2023Inclui referênciasResumo: A identificação e caracterização de sementes nativas representam um desafio para o setor florestal devido à variabilidade de características morfobiométricas. Atualmente, as metodologias para a análise biométrica de sementes florestais são realizadas por especialistas humanos utilizando métodos tradicionais de medições, como os paquímetros e variáveis baseadas em tamanho. Nesse contexto, concebeu-se uma nova metodologia empregando técnicas de processamento de imagens digitais e aprendizado de máquina com base em características externas das sementes para possibilitar a identificação de espécies florestais. A pesquisa foi dividida em três capítulos distintos. No primeiro capítulo foi realizada uma análise bibliométrica para quantificar e analisar os estudos científicos que abordam a análise de imagens e o aprendizado de máquina aplicados às sementes, e com isso apontar os principais tópicos e lacunas existentes para pesquisas com sementes florestais com esse enfoque. Os resultados indicam um aumento significativo de publicações a partir de 2017, com foco predominante em espécies de culturas agrícolas. Esses estudos estão direcionados principalmente para a classificação, identificação/detecção de cultivares e avaliação da qualidade das sementes, em que apenas 6,6% das publicações abordam espécies florestais, evidenciando a necessidade de mais pesquisas nesse campo com espécies nativas. No segundo capítulo foi proposta uma metodologia de captura e processamento de imagens para caracterização e diferenciação de espécies florestais nativas. Os resultados demonstraram que a análise de imagens de sementes, por meio dessa metodologia, contribuiu para a caracterização e diferenciação de espécies florestais nativas do Brasil, o que apresenta implicações diretas nos aspectos silviculturais, ecológicos e genéticos. No terceiro capítulo foram aplicados diferentes classificadores de aprendizado de máquina associados à análise de imagens para identificar espécies florestais nativas com base em características morfobiométricas das sementes. Os resultados revelaram que é possível identificar espécies florestais nativas com taxa satisfatória de acurácia usando imagens de sementes e aprendizado de máquina. Recomenda-se o classificador de árvores de decisão para a identificação de espécies. Os resultados fornecem subsídios importantes para aprimorar a caracterização e identificação de espécies, o que pode ser aplicado em diversos campos. Por fim, este trabalho contribui para identificar espécies florestais nativas, por meio do desenvolvimento de uma metodologia de análise e processamento de imagens e da aplicação de técnicas de aprendizado de máquina em sementes florestais.Abstract: The identification and characterization of native seeds represent a challenge for the forest sector due to the variability of morphobiometric characteristics. Currently, methodologies for the biometric analysis of forest seeds are carried out by human specialists using traditional measurement methods, such as calipers and variables based on size. In this context, a new methodology was conceived using techniques of digital image processing and machine learning based on external characteristics of the seeds to enable the identification of forest species. The research was divided into three distinct chapters. In the first chapter, a bibliometric analysis was carried out to quantify and analyze scientific studies that address image analysis and machine learning applied to seeds, and thereby point out the main topics and existing gaps for research with forest seeds with this focus. The results indicate a significant increase in publications from 2017 onwards, with a predominant focus on agricultural crop species. These studies are mainly focused on classification, identification/detection of cultivars and evaluation of seed quality, in which only 6.6% of publications address forest species, highlighting the need for further research in this field with native species. In the second chapter, a methodology for capturing and processing images for the characterization and differentiation of native forest species was proposed. The results showed that the analysis of seed images, using this methodology, contributed to the characterization and differentiation of forest species native to Brazil, which has direct implications for silvicultural, ecological, and genetic aspects. In the third chapter, different machine learning classifiers associated with image analysis were applied to identify native forest species based on morphobiometric characteristics of seeds. The results revealed that it is possible to identify native forest species with a satisfactory rate of accuracy using seed images and machine learning. The decision tree classifier is recommended for species identification. The results provide important subsidies to improve the characterization and identification of species, which can be applied in several fields. Finally, this work contributes to identify native forest species, through the development of an image analysis and processing methodology and the application of machine learning techniques in forest seeds